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cs.DC2026
Energy-Efficient LLM Serving via Disaggregated Attention--FFN and Flexible Frequency Scaling
Cunchen Hu, Liangliang Xu, Tian Liu +9
Large language model (LLM) serving spans diverse applications with stringent service-level objectives (SLOs), often requiring GPUs to run at maximum frequencies and increasing ener…
cs.DC2026
SwiftCache: Efficient LLM Serving for Multi-turn Conversations with Heterogeneous KV Cache Sharing
Jianmin Hu, Minxian Xu, Sa Wang +5
Multi-turn conversation is a fundamental scenario in LLM applications, widely used in chatbots and AI agents. As the conversation evolves, historical tokens accumulate continuously…
cs.DC2026
Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
Minxian Xu, Jingfeng Wu, Shengye Song +16
The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, t…